Estimating vehicle velocities using linear-parameter-varying and gain varying scheduling theories

ABSTRACT

A system and a method for dynamically estimating the vehicle longitudinal and lateral velocities based on information gathered from four sensors measuring the longitudinal acceleration, lateral acceleration, wheel speed and yaw rate. The present invention provides a linear-parameter-varying state observer in conjunction with a gain scheduled state observer to provide good estimation of the vehicle motion in linear and non-linear ranges. The present invention is not dependent on variations in vehicle parameters, requires low computing power, and achieves improved estimation by adjusting the observer gains according to the changing yaw rate.

TECHNICAL FIELD

[0001] The present invention relates generally to estimatinglongitudinal and lateral velocities of a motor vehicle and moreparticularly to dynamically estimating longitudinal and lateralvelocities.

BACKGROUND OF THE INVENTION

[0002] In recent years, there has been a tremendous increase in interestin advanced safety features in motor vehicles. This has led to thedevelopment of advanced vehicle chassis control systems, such asanti-lock brakes (ABS), traction control (TC), four-wheel steering(4WS), electronic stability program (ESP) to name but a few. To controla motor vehicle's motion it is necessary for the control system to knowthe vehicle's dynamic status, in terms of longitudinal velocity andacceleration, lateral velocity and acceleration, yaw rate, wheel speeds,and other parameters.

[0003] Information of all the dynamic signals can be obtained fromsensor measurements. However, for an accurate estimate of the vehicle'sparameters, the number of sensors needed is quite large and the sensorsare expensive. A large number of expensive sensors add unwanted cost andweight to the motor vehicle. The sensors also occupy valuable packagingspace on the vehicle.

[0004] Various algorithms have been proposed for estimating the vehicledynamics. A typical method uses linear techniques, such as Kalmanfiltering. However, this approach has limited success because of theinherent non-linearity of vehicle dynamics. Other estimation methodsdepend heavily on the accuracy of a model for tire dynamics as well asinformation from a road/tire friction coefficient. The computing powerrequired in such detailed models easily exceeds the computing poweravailable in a normal vehicle engine control unit (ECU).

[0005] There is a need for a robust velocity estimation method havingrelatively low computing power requirements and at the same timeproviding accurate vehicle dynamic estimations.

SUMMARY OF THE INVENTION

[0006] The present invention is a system and method for dynamicallyestimating the longitudinal and lateral velocities of a motor vehicle.It presents a robust velocity estimation method having low computingpower requirements and therefore fits well within the technological andfinancial constraints for developing vehicle control systems. Thepresent invention accesses the information of vehicle dynamic signalsusing a minimum number of low-cost, off-the-shelf sensors that measurelongitudinal acceleration, lateral acceleration, wheel speed and yawrate. The present invention provides a gain scheduledlinear-parameter-varying (LPV) state observer for vehicle longitudinaland lateral velocities based on information gathered from just a fewsensors.

[0007] It is an object of the present invention to model a vehicle'sdynamic behavior. It is another object of the present invention toestimate longitudinal and lateral velocities in linear and non-linearranges of a vehicle's motion.

[0008] It is a further object of the present invention to accuratelymodel the vehicle's dynamic behavior using low computing power from thevehicle's microprocessor. It is still a further object of the presentinvention to provide a robust model of the vehicle's dynamics that isindependent of other vehicle parameters such as vehicle mass, center ofgravity, moment of inertia and tire cornering stiffness.

[0009] Other objects and advantages of the present invention will becomeapparent upon reading the following detailed description and appendedclaims, and upon reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] For a more complete understanding of this invention, referenceshould now be had to the embodiments illustrated in greater detail inthe accompanying drawings and described below by way of examples of theinvention. In the drawings:

[0011]FIG. 1 is a schematic illustration of a vehicle showing thevehicle coordinate system associated with the method and system of thepresent invention;

[0012]FIG. 2 is a block diagram of the system of the present invention;and

[0013]FIG. 3 is a flow chart of the method of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0014] Referring to FIG. 1, a coordinate system used as a reference forthe present invention is shown. A vehicle 10 has a center of gravity,CG, about which the yaw rate, <, is referenced as well as the lateralvelocity, V_(x), and the longitudinal velocity, V_(y).

[0015] Referring now to FIG. 2 a block diagram of the system 50 of thepresent invention is shown. Sensors 12, 14, 16 and 18 measuringlongitudinal acceleration, A_(x), lateral acceleration, A_(y), yaw rate,<, and wheel speed are found on the vehicle (not shown). The signalsfrom the sensors 12, 14, 16, and 18 undergo signal processing 20techniques. One skilled in the art is capable of normal processing ofsensor information, which typically includes noise filtering and offsetcompensation. Several examples of signal processing can be found in U.S.Pat. Nos. 5,742,918 and 5,809,434. It is desirable for the presentinvention to use accurate signal information from the sensors in orderto estimate accurate velocities.

[0016] The compensated signals are sent to the Linear-Parameter-Varying(LPV) state observer 22 of the present invention. The processed yawrate, Ψ, is sent independently to a gain scheduler 24, which is also fedinto the LPV 22. Finally, the estimated vehicle velocities from the LPV22 are fed into the vehicle control system 26 and used for controllingsystems such as traction control, electronic stability program, or anyother system in the vehicle that is used to improve the performance,handling, and safety of the vehicle.

[0017] The LPV state observer 22 calculates a signal from the processedsensor signals that is used to estimate the lateral and longitudinalvelocities of the vehicle. The LPV state observer 22 uses the followingequation: $\begin{matrix}{\begin{bmatrix}\frac{{\hat{V}}_{x}}{t} \\\frac{{\hat{V}}_{y}}{t}\end{bmatrix} = {{\begin{bmatrix}0 & \Psi \\{- \Psi} & 0\end{bmatrix}\begin{bmatrix}{\hat{V}}_{x} \\{\hat{V}}_{y}\end{bmatrix}} + {\begin{bmatrix}L_{1} \\L_{2}\end{bmatrix}( {Y_{m} - {\begin{bmatrix}1 & 0\end{bmatrix}\begin{bmatrix}{\hat{V}}_{x} \\{\hat{V}}_{y}\end{bmatrix}}} )} + {\begin{bmatrix}1 & 0 \\0 & 1\end{bmatrix}\begin{bmatrix}A_{x} \\A_{y}\end{bmatrix}}}} & 1\end{matrix}$

[0018] V′_(x) _(and V′) _(y) are the estimations of the vehiclevelocities V_(x) and V_(y) respectively. L₁ and L₂ are time-varyingobserver gains. The signal Y_(m) is calculated from availableinformation from the vehicle sensors and is used as a measurement of thelongitudinal velocity.

[0019] The LPV state observer 22 has gains, L₁ and L₂. Determining L₁and L₂ is a key for best estimation results, and one way to do this isto schedule L₁ and L₂ by the gain scheduler 24 according to the yaw rateΨ and other parameters in the following equations: $\begin{matrix}\begin{matrix}{L_{1} = {2\lambda}} \\{L_{2} = {{\frac{\lambda^{2}}{\Psi_{L}} - {\Psi,\quad \Psi_{L}}} = \{ {\begin{matrix}{{\Psi,}\quad} \\{{\Psi_{0\quad}{{sign}(\Psi)},}\quad}\end{matrix}\begin{matrix} {for} \middle| \Psi \middle| {\geq \Psi_{0}}  \\ {for} \middle| \Psi \middle| {< \Psi_{0}} \end{matrix}} }}\end{matrix} & {2\quad {and}\quad 3}\end{matrix}$

[0020] is a positive constant related to the closed-loop observer polelocation, Ψ_(o) is a positive constant which serves a dual purpose ofcompensating sensor signal vibration and avoiding a zero yaw rate. Oneskilled in the art is capable of tuning these two parameters accordingto vehicle attributes, sensor specifications, and vehicle dynamic statusin order to achieve the best estimation results.

[0021] Referring now to FIG. 3, the method 100 of the present inventionbegins 102 by reading 104 the signals from the sensors. The sensorsignals are compensated 106 as is known in the art by signal processingtechniques. Scheduled observer gains are calculated 108 using the yawrate and other parameters as described in the formula above.

[0022] The vehicle velocity estimates are calculated 110 using the LPVstate observer equations described above. The estimated values are fed112 to the vehicle control systems, where they are used according toeach systems need.

[0023] The present invention provides a system and a method fordynamically estimating the vehicle longitudinal and lateral velocitiesbased on information gathered from four sensors measuring thelongitudinal acceleration, lateral acceleration, wheel speed and yawrate. The present invention provides a linear-parameter-varying stateobserver in conjunction with a gain scheduled state observer to providegood estimation of the vehicle motion in linear and non-linear ranges.The present invention is not dependent on variations in vehicleparameters, requires low computing power, and achieves improvedestimation by adjusting the observer gains according to the changing yawrate.

[0024] The invention covers all alternatives, modifications, andequivalents, as may be included within the spirit and scope of theappended claims.

What is claimed is:
 1. A method for estimating longitudinal and lateralvelocities in a motor vehicle comprising the steps of: sensing lateralacceleration, A_(x), on the motor vehicle; sensing longitudinalacceleration A_(y), on the motor vehicle; sensing yaw rate, Ψ, on themotor vehicle; determining time-varying gains, L1 and L2, according tothe sensed yaw rate and the following formulas; $\begin{matrix}\begin{matrix}{L_{1} = {2\lambda}} \\{L_{2} = {{\frac{\lambda^{2}}{\Psi_{L}} - {\Psi,\quad \Psi_{L}}} = \{ {\begin{matrix}{{\Psi,}\quad} \\{{\Psi_{0\quad}{{sign}(\Psi)},}\quad}\end{matrix}\begin{matrix} {for} \middle| \Psi \middle| {\geq \Psi_{0}}  \\ {for} \middle| \Psi \middle| {< \Psi_{0}} \end{matrix}} }}\end{matrix} & {2\quad {and}\quad 3}\end{matrix}$

where λ is a positive constant, Ψo is a positive constant, and “sign”represents the sign of the way rate; determining estimates of thelateral velocity, V′x and the longitudinal velocity V′y, in accordancewith the following equation; $\begin{matrix}{\begin{bmatrix}\frac{{\hat{V}}_{x}}{t} \\\frac{{\hat{V}}_{y}}{t}\end{bmatrix} = {{\begin{bmatrix}0 & \Psi \\{- \Psi} & 0\end{bmatrix}\begin{bmatrix}{\hat{V}}_{x} \\{\hat{V}}_{y}\end{bmatrix}} + {\begin{bmatrix}L_{1} \\L_{2}\end{bmatrix}( {Y_{m} - {\begin{bmatrix}1 & 0\end{bmatrix}\begin{bmatrix}{\hat{V}}_{x} \\{\hat{V}}_{y}\end{bmatrix}}} )} + {\begin{bmatrix}1 & 0 \\0 & 1\end{bmatrix}\begin{bmatrix}A_{x} \\A_{y}\end{bmatrix}}}} & 1\end{matrix}$

where Ym is a signal representing a measurement of the longitudinalwheel velocity.
 2. The method as claimed in claim 1 wherein Ym iscalculated from wheel velocity information sensed on the motor vehicle.3. The method as claimed in claim 1 further comprising the step oftuning Ψo and λ according to attributes specific to the motor vehicle,specifications specific to sensors and a status of vehicle dynamics ofthe motor vehicle.
 4. The method as claimed in claim 1 furthercomprising the steps of: filtering noise from the sensed lateralacceleration, longitudinal acceleration and yaw rate; and offsetcompensating the sensed lateral acceleration, longitudinal accelerationand yaw rate.
 5. A system for estimating vehicle lateral velocity andlongitudinal velocity comprising: a plurality of sensors attached to avehicle for sensing predetermined vehicle parameters including lateralacceleration, Ax, longitudinal acceleration, Ay and yaw rate, Ψ; amicrocontroller in electrical communication with said plurality ofsensors, said microcontroller being operative to: determine time-varyinggains, L1 and L2, according to the sensed yaw rate and the followingformulas; $\begin{matrix}\begin{matrix}{L_{1} = {2\lambda}} \\{L_{2} = {{\frac{\lambda^{2}}{\Psi_{L}} - {\Psi,\quad \Psi_{L}}} = \{ {\begin{matrix}{{\Psi,}\quad} \\{{\Psi_{0\quad}{{sign}(\Psi)},}\quad}\end{matrix}\begin{matrix} {for} \middle| \Psi \middle| {\geq \Psi_{0}}  \\ {for} \middle| \Psi \middle| {< \Psi_{0}} \end{matrix}} }}\end{matrix} & {2\quad {and}\quad 3}\end{matrix}$

where λ is a positive constant, Ψo is a positive constant, and “sign”represents the sign of the way rate; and determine estimates of thelateral velocity, V′x and the longitudinal velocity V′y, in accordancewith the following equation; $\begin{matrix}{\begin{bmatrix}\frac{{\hat{V}}_{x}}{t} \\\frac{{\hat{V}}_{y}}{t}\end{bmatrix} = {{\begin{bmatrix}0 & \Psi \\{- \Psi} & 0\end{bmatrix}\begin{bmatrix}{\hat{V}}_{x} \\{\hat{V}}_{y}\end{bmatrix}} + {\begin{bmatrix}L_{1} \\L_{2}\end{bmatrix}( {Y_{m} - {\begin{bmatrix}1 & 0\end{bmatrix}\begin{bmatrix}{\hat{V}}_{x} \\{\hat{V}}_{y}\end{bmatrix}}} )} + {\begin{bmatrix}1 & 0 \\0 & 1\end{bmatrix}\begin{bmatrix}A_{x} \\A_{y}\end{bmatrix}}}} & 1\end{matrix}$

where Ym is a signal representing a measurement of the longitudinalwheel velocity.
 6. The system as claimed in claim 5 further comprising asignal processor for filtering noise and performing offset compensationof signals sensed by said plurality of sensors.
 7. A method forestimating longitudinal and lateral velocities in a motor vehiclecomprising the steps of: sensing lateral acceleration, Ax, on the motorvehicle; sensing longitudinal acceleration Ay, on the motor vehicle;sensing yaw rate, Ψ, on the motor vehicle; filtering noise from thesensed lateral acceleration, longitudinal acceleration and yaw rate; andoffset compensating the sensed lateral acceleration, longitudinalacceleration and yaw rate; determining time-varying gains, L1 and L2,according to the sensed yaw rate and the following formulas;$\begin{matrix}\begin{matrix}{L_{1} = {2\lambda}} \\{L_{2} = {{\frac{\lambda^{2}}{\Psi_{L}} - {\Psi,\quad \Psi_{L}}} = \{ {\begin{matrix}{{\Psi,}\quad} \\{{\Psi_{0\quad}{{sign}(\Psi)},}\quad}\end{matrix}\begin{matrix} {for} \middle| \Psi \middle| {\geq \Psi_{0}}  \\ {for} \middle| \Psi \middle| {< \Psi_{0}} \end{matrix}} }}\end{matrix} & {2\quad {and}\quad 3}\end{matrix}$

where λ is a positive constant, Ψo is a positive constant, and “sign”represents the sign of the way rate; tuning Ψo and λ according toattributes specific to the motor vehicle, specifications specific tosensors and a status of vehicle dynamics of the motor vehicle;determining estimates of the lateral velocity, V′x and the longitudinalvelocity V′y, in accordance with the following equation; $\begin{matrix}{\begin{bmatrix}\frac{{\hat{V}}_{x}}{t} \\\frac{{\hat{V}}_{y}}{t}\end{bmatrix} = {{\begin{bmatrix}0 & \Psi \\{- \Psi} & 0\end{bmatrix}\begin{bmatrix}{\hat{V}}_{x} \\{\hat{V}}_{y}\end{bmatrix}} + {\begin{bmatrix}L_{1} \\L_{2}\end{bmatrix}( {Y_{m} - {\begin{bmatrix}1 & 0\end{bmatrix}\begin{bmatrix}{\hat{V}}_{x} \\{\hat{V}}_{y}\end{bmatrix}}} )} + {\begin{bmatrix}1 & 0 \\0 & 1\end{bmatrix}\begin{bmatrix}A_{x} \\A_{y}\end{bmatrix}}}} & 1\end{matrix}$

where Ym is a signal representing a measurement of the longitudinalwheel velocity.